1. Field of the Invention
The present invention relates to continuous glucose monitoring (CGM) and, more specifically, to a retrospective “retrofitting” algorithm to improve accuracy and precision of glucose concentration levels by exploiting available reference glucose measurements in the blood and a new constrained regularized deconvolution method. The inputs of the retrofitting algorithm are: a CGM time series; some reference blood glucose (BG) measurements; a model of blood to interstitial glucose kinetics; and a model of the deterioration in time of sensor accuracy. The outputs are: an improved (“retrofitted”) quasi-continuous glucose concentration signal that is better (in terms of both accuracy and precision) than the CGM trace originally measured by the sensor, together with its confidence interval; a retrospectively calibrated CGM time series; and a set of CGM and BG references data that are discarded from the analysis because labeled as unreliable data or outliers.
2. Description of the Related Art
Diabetes is a disease that causes abnormal glycemic values due to the inability of the pancreas to produce insulin (Type 1 diabetes) or to the inefficiency of insulin secretion and action (Type 2 diabetes). Patients affected by diabetes need to monitor their blood glucose (BG) level during all day in order to control it and take countermeasures to keep it inside the normal range of 70-180 mg/dl as much as possible. Diabetic patients are forced to take exogenous insulin infusions or drugs, whose scheduling and dosages are calculated on the basis of BG measurements.
According to the current gold standard, BG measurements can be collected in two main ways: i) during daily life by means of capillary finger-pricks, i.e. self monitoring blood glucose (SMBG) 4-5 times per day at most; ii) during hospitalized clinical trials, by means of gold standard laboratory instruments. Both these BG monitoring systems are reasonably accurate. However, sampling in the blood can be done only sporadically, and, as a result, fast fluctuations of the patient's glucose concentrations can result invisible. FIG. 1 shows a conceptual simulated example where the sparseness of SMBG measurements (triangles) does not allow to fully capture glucose variations evident in the continuous-time glycemic profile (dashed line) and even impair the detection of some hypo/hyperglycemic events (e.g. around times 10 and 30).
In the last 15 years, continuous glucose monitoring (CGM) sensors have been introduced. Differently from BG measurement systems, these devices measure glucose in the interstitial fluid rather than in the circulation, reducing the invasiveness and allowing the visualization of real-time glucose values every 1-5 minutes for several consecutive days. CGM sensors provide a more comprehensive picture of glucose fluctuations, evidencing critical episodes that could be undetectable using SMBG systems. However, CGM devices still suffer for some inaccuracy. In fact, when compared to BG references provided by SMBG or laboratory devices, CGM profiles sometimes present transient or systematic under/overestimations, outlier samples and portions of missing data. FIG. 2 shows a conceptual example where a representative CGM time series (dots) is compared to the same SMBG references (triangles) and true simulated glycemia (dashed line) already presented in FIG. 1. A 1st order interpolation of CGM values (dots) is reported to facilitate visual comparison. CGM has a much higher temporal resolution (5 min sampling) than SMBG, but sometimes exhibits systematic under/overestimations of the true concentration. Differently from SMBG references, CGM allows a fair assessment of glucose fluctuations. However, systematic overestimation of CGM is visible from hour 20 to 30, together with a transient underestimation at time 12. Obviously, lack of accuracy of CGM is detrimental to its clinical use and, at present time, is recognized in the research community as a bottleneck for several practical applications.
In this document we describe a retrospective retrofitting procedure that creates a quasi-continuous glucose concentration signal, which is better, in terms of both accuracy and precision, than the CGM trace originally measured by the CGM sensor. This is done by exploiting few, sparse but accurate, BG reference samples (that could be either SMBGs or BG values obtained via laboratory instruments) and frequent quasi-continuous CGM data, which can be noisy and biased. The procedure incorporates an original constrained-deconvolution approach and returns in output a quasi-continuous glucose concentration profile, hereafter referred as the retrofitted glucose concentration time-series, which tackles in great part the accuracy and precision issues of the original CGM sensor data.
Some methods to increase accuracy and precision of CGM time series are available.                i) The first class of methods aims at improving online the precision of CGM output by reducing the effect of measurement noise. The method proposed by Palerm and Bequette (Diabetes Technol Ther 2005) is based on a Kalman filter with fixed parameters. The methods of Facchinetti et al. (IEEE Trans Biomed Eng 2010 and 2011), in particular, exploit a Bayesian approach to denoise CGM data taking into account interindividual and intraindividual variability of the signal-to-noise ratio. These methods do not take into account any physiological/technological model to compensate for possible lack of accuracy.        ii) The second class of methods is aimed to improve accuracy of CGM data in real time. In a first approach, employed by Knobbe and Buckingham (Diabetes Technol Ther 2005) and Facchinetti et al. (Diabetes Technol Ther 2010), a state-space Bayesian framework exploiting a priori knowledge on the variability of the sensitivity of CGM sensor and a model of the blood-to-interstitium glucose kinetics was adopted and implemented via extended Kalman filtering. In a second approach, used in Guerra et al. (IEEE Trans Biomed Eng 2012), CGM data were enhanced by using a deconvolution-based method relying on a physiological model of the blood to interstitial fluid glucose kinetics. In a third approach, a raw deconvolution is used to improve CGM data accuracy (Kovatchev B. and King C., U.S. Patent Appl. 2008/0314395 A1, Dec. 25, 2008). Other approaches are based e.g. on multiple local models (Barcelo-Rico et al., Diabetes Technol Ther 2012), on autoregressive models (Leal et al., J Diabetes Sci Technol 2010), fixed-delay models and FIR filters (Kanderian S. and Steil G. M., U.S. Patent 2007/0173761 A1, Jul. 26, 2007).        
The methods under i) address the problem of precision of CGM data, but not that of the possible lack/loss of accuracy. The methods under ii) consider the problem of possible lack of accuracy, but not that of lack of precision. In addition, in order to work online, the methods of the second class are fed with average/population parameters, which could make them suboptimal. Furthermore, all the methods above described (both under i) and ii)) are causal and are thus unable to take into account also future data, a possibility that should be usefully exploited in a retrospective analysis setting.